SUPREET KAUR

● Fraud and Risk Analysis for UK based client

Working for a UK based Insurance firm.

1. Project Title: Predictive Fraud Detection using SAS

2. Project Overview: Developed a fraud detection system using SAS to predict the likelihood of fraudulent insurance claims. Applied data preprocessing, basic business rules, and predictive modeling techniques to identify suspicious behaviors and improve fraud detection accuracy.

3. Tools and Technologies:

· SAS (Base SAS)

· Microsoft Excel

4. Project Details:

· Extracted claim-related data from Excel sheets using SAS procedures.

· Preprocessed and filtered the data to focus on relevant information for analysis.

· Applied data manipulation techniques to identify potential fraudulent behaviors and patterns.

· Implemented business rules to flag suspicious claims based on specific conditions.

· Built predictive models using SAS procedures to enhance fraud detection capabilities.

· Generated reports on a regular basis to showcase fraudulent claim trends.

5. Key Achievements:

· Successfully developed a fraud detection system that improved the accuracy of identifying potential fraudulent claims.

· Utilized SAS's advanced analytics capabilities to build predictive models based on historical patterns.

· Contributed to the development of weekly and quarterly reports that aided stakeholders in making informed decisions.

6. Skills Demonstrated:

· Data Extraction and Preprocessing

· Data Analysis and Manipulation in SAS

· Business Rule Implementation

· Predictive Modeling with SAS

· Report Generation and Communication

7. Outcome: The project enhanced my proficiency in SAS, data analysis, and predictive modeling techniques. It also provided valuable experience in applying business rules for fraud detection and generating actionable insights from data.

2. Project title:Analyzing Claims using SAS

• Imported data containing policyholder details, claim dates, types,amounts,etc.
• Used SAS procedures like proc means,proc summary,proc freq, compbl function, etc. for data manipulation and claim type frequencies exploration.
• Created visualizations (e.g., histograms, box plots) for data understanding.
• Interpreted results and provide actionable recommendations for process efficiency and policy optimization based on the analysis.